# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Util functions related to pycocotools and COCO eval."""

import copy
import json

from absl import logging
import numpy as np
from PIL import Image
from pycocotools import coco
from pycocotools import mask as mask_api
import six
import tensorflow as tf, tf_keras

from official.common import dataset_fn
from official.vision.dataloaders import tf_example_decoder
from official.vision.ops import box_ops
from official.vision.ops import mask_ops


class COCOWrapper(coco.COCO):
  """COCO wrapper class.

  This class wraps COCO API object, which provides the following additional
  functionalities:
    1. Support string type image id.
    2. Support loading the ground-truth dataset using the external annotation
       dictionary.
    3. Support loading the prediction results using the external annotation
       dictionary.
  """

  def __init__(self, eval_type='box', annotation_file=None, gt_dataset=None):
    """Instantiates a COCO-style API object.

    Args:
      eval_type: either 'box' or 'mask'.
      annotation_file: a JSON file that stores annotations of the eval dataset.
        This is required if `gt_dataset` is not provided.
      gt_dataset: the ground-truth eval datatset in COCO API format.
    """
    if ((annotation_file and gt_dataset) or
        ((not annotation_file) and (not gt_dataset))):
      raise ValueError('One and only one of `annotation_file` and `gt_dataset` '
                       'needs to be specified.')

    if eval_type not in ['box', 'mask']:
      raise ValueError('The `eval_type` can only be either `box` or `mask`.')

    coco.COCO.__init__(self, annotation_file=annotation_file)
    self._eval_type = eval_type
    if gt_dataset:
      self.dataset = gt_dataset
      self.createIndex()

  def loadRes(self, predictions):
    """Loads result file and return a result api object.

    Args:
      predictions: a list of dictionary each representing an annotation in COCO
        format. The required fields are `image_id`, `category_id`, `score`,
        `bbox`, `segmentation`.

    Returns:
      res: result COCO api object.

    Raises:
      ValueError: if the set of image id from predctions is not the subset of
        the set of image id of the ground-truth dataset.
    """
    res = coco.COCO()
    res.dataset['images'] = copy.deepcopy(self.dataset['images'])
    res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])

    image_ids = [ann['image_id'] for ann in predictions]
    if set(image_ids) != (set(image_ids) & set(self.getImgIds())):
      raise ValueError('Results do not correspond to the current dataset!')
    for ann in predictions:
      x1, x2, y1, y2 = [ann['bbox'][0], ann['bbox'][0] + ann['bbox'][2],
                        ann['bbox'][1], ann['bbox'][1] + ann['bbox'][3]]
      if self._eval_type == 'box':
        ann['area'] = ann['bbox'][2] * ann['bbox'][3]
        ann['segmentation'] = [
            [x1, y1, x1, y2, x2, y2, x2, y1]]
      elif self._eval_type == 'mask':
        ann['area'] = mask_api.area(ann['segmentation'])

    res.dataset['annotations'] = copy.deepcopy(predictions)
    res.createIndex()
    return res


def convert_predictions_to_coco_annotations(predictions):
  """Converts a batch of predictions to annotations in COCO format.

  Args:
    predictions: a dictionary of lists of numpy arrays including the following
      fields. 'K' below denotes the maximum number of instances per image.
      Required fields:
        - source_id: a list of numpy arrays of int or string of shape
            [batch_size].
        - detection_boxes: a list of numpy arrays of float of shape
            [batch_size, K, 4], where coordinates are in the original image
            space (not the scaled image space).
        - detection_classes: a list of numpy arrays of int of shape
            [batch_size, K].
        - detection_scores: a list of numpy arrays of float of shape
            [batch_size, K].
      Optional fields:
        - detection_masks: a list of numpy arrays of float of shape
            [batch_size, K, mask_height, mask_width].
        - detection_keypoints: a list of numpy arrays of float of shape
            [batch_size, K, num_keypoints, 2]

  Returns:
    coco_predictions: prediction in COCO annotation format.
  """
  coco_predictions = []
  num_batches = len(predictions['source_id'])
  max_num_detections = predictions['detection_classes'][0].shape[1]
  use_outer_box = 'detection_outer_boxes' in predictions
  for i in range(num_batches):
    predictions['detection_boxes'][i] = box_ops.yxyx_to_xywh(
        predictions['detection_boxes'][i])
    if use_outer_box:
      predictions['detection_outer_boxes'][i] = box_ops.yxyx_to_xywh(
          predictions['detection_outer_boxes'][i])
      mask_boxes = predictions['detection_outer_boxes']
    else:
      mask_boxes = predictions['detection_boxes']

    batch_size = predictions['source_id'][i].shape[0]
    if 'detection_keypoints' in predictions:
      # Adds extra ones to indicate the visibility for each keypoint as is
      # recommended by MSCOCO. Also, convert keypoint from [y, x] to [x, y]
      # as mandated by COCO.
      num_keypoints = predictions['detection_keypoints'][i].shape[2]
      coco_keypoints = np.concatenate(
          [
              predictions['detection_keypoints'][i][..., 1:],
              predictions['detection_keypoints'][i][..., :1],
              np.ones([batch_size, max_num_detections, num_keypoints, 1]),
          ],
          axis=-1,
      ).astype(int)
    for j in range(batch_size):
      if 'detection_masks' in predictions:
        image_masks = mask_ops.paste_instance_masks(
            predictions['detection_masks'][i][j],
            mask_boxes[i][j],
            int(predictions['image_info'][i][j, 0, 0]),
            int(predictions['image_info'][i][j, 0, 1]),
        )
        binary_masks = (image_masks > 0.0).astype(np.uint8)
        encoded_masks = [
            mask_api.encode(np.asfortranarray(binary_mask))
            for binary_mask in list(binary_masks)
        ]
      for k in range(max_num_detections):
        ann = {}
        ann['image_id'] = predictions['source_id'][i][j]
        ann['category_id'] = predictions['detection_classes'][i][j, k]
        ann['bbox'] = predictions['detection_boxes'][i][j, k]
        ann['score'] = predictions['detection_scores'][i][j, k]
        if 'detection_masks' in predictions:
          ann['segmentation'] = encoded_masks[k]
        if 'detection_keypoints' in predictions:
          ann['keypoints'] = coco_keypoints[j, k].flatten().tolist()
        coco_predictions.append(ann)

  for i, ann in enumerate(coco_predictions):
    ann['id'] = i + 1

  return coco_predictions


def convert_groundtruths_to_coco_dataset(groundtruths, label_map=None):
  """Converts ground-truths to the dataset in COCO format.

  Args:
    groundtruths: a dictionary of numpy arrays including the fields below.
      Note that each element in the list represent the number for a single
      example without batch dimension. 'K' below denotes the actual number of
      instances for each image.
      Required fields:
        - source_id: a list of numpy arrays of int or string of shape
          [batch_size].
        - height: a list of numpy arrays of int of shape [batch_size].
        - width: a list of numpy arrays of int of shape [batch_size].
        - num_detections: a list of numpy arrays of int of shape [batch_size].
        - boxes: a list of numpy arrays of float of shape [batch_size, K, 4],
            where coordinates are in the original image space (not the
            normalized coordinates).
        - classes: a list of numpy arrays of int of shape [batch_size, K].
      Optional fields:
        - is_crowds: a list of numpy arrays of int of shape [batch_size, K]. If
            th field is absent, it is assumed that this instance is not crowd.
        - areas: a list of numy arrays of float of shape [batch_size, K]. If the
            field is absent, the area is calculated using either boxes or
            masks depending on which one is available.
        - masks: a list of numpy arrays of string of shape [batch_size, K],
    label_map: (optional) a dictionary that defines items from the category id
      to the category name. If `None`, collect the category mapping from the
      `groundtruths`.

  Returns:
    coco_groundtruths: the ground-truth dataset in COCO format.
  """
  source_ids = np.concatenate(groundtruths['source_id'], axis=0)
  heights = np.concatenate(groundtruths['height'], axis=0)
  widths = np.concatenate(groundtruths['width'], axis=0)
  gt_images = [{'id': int(i), 'height': int(h), 'width': int(w)} for i, h, w
               in zip(source_ids, heights, widths)]

  gt_annotations = []
  num_batches = len(groundtruths['source_id'])
  for i in range(num_batches):
    logging.log_every_n(
        logging.INFO,
        'convert_groundtruths_to_coco_dataset: Processing annotation %d', 100,
        i)
    max_num_instances = groundtruths['classes'][i].shape[1]
    batch_size = groundtruths['source_id'][i].shape[0]
    for j in range(batch_size):
      num_instances = groundtruths['num_detections'][i][j]
      if num_instances > max_num_instances:
        logging.warning(
            'num_groundtruths is larger than max_num_instances, %d v.s. %d',
            num_instances, max_num_instances)
        num_instances = max_num_instances
      for k in range(int(num_instances)):
        ann = {}
        ann['image_id'] = int(groundtruths['source_id'][i][j])
        if 'is_crowds' in groundtruths:
          ann['iscrowd'] = int(groundtruths['is_crowds'][i][j, k])
        else:
          ann['iscrowd'] = 0
        ann['category_id'] = int(groundtruths['classes'][i][j, k])
        boxes = groundtruths['boxes'][i]
        ann['bbox'] = [
            float(boxes[j, k, 1]),
            float(boxes[j, k, 0]),
            float(boxes[j, k, 3] - boxes[j, k, 1]),
            float(boxes[j, k, 2] - boxes[j, k, 0])]
        if 'areas' in groundtruths:
          ann['area'] = float(groundtruths['areas'][i][j, k])
        else:
          ann['area'] = float(
              (boxes[j, k, 3] - boxes[j, k, 1]) *
              (boxes[j, k, 2] - boxes[j, k, 0]))
        if 'masks' in groundtruths:
          if isinstance(groundtruths['masks'][i][j, k], tf.Tensor):
            mask = Image.open(
                six.BytesIO(groundtruths['masks'][i][j, k].numpy()))
          else:
            mask = Image.open(
                six.BytesIO(groundtruths['masks'][i][j, k]))
          np_mask = np.array(mask, dtype=np.uint8)
          np_mask[np_mask > 0] = 255
          encoded_mask = mask_api.encode(np.asfortranarray(np_mask))
          ann['segmentation'] = encoded_mask
          # Ensure the content of `counts` is JSON serializable string.
          if 'counts' in ann['segmentation']:
            ann['segmentation']['counts'] = six.ensure_str(
                ann['segmentation']['counts'])
          if 'areas' not in groundtruths:
            ann['area'] = mask_api.area(encoded_mask)
        if 'keypoints' in groundtruths:
          keypoints = groundtruths['keypoints'][i]
          coco_keypoints = []
          num_valid_keypoints = 0
          for z in range(len(keypoints[j, k, :, 1])):
            # Convert from [y, x] to [x, y] as mandated by COCO.
            x = float(keypoints[j, k, z, 1])
            y = float(keypoints[j, k, z, 0])
            coco_keypoints.append(x)
            coco_keypoints.append(y)
            if tf.math.is_nan(x) or tf.math.is_nan(y) or (
                x == 0 and y == 0):
              visibility = 0
            else:
              visibility = 2
              num_valid_keypoints = num_valid_keypoints + 1
            coco_keypoints.append(visibility)
          ann['keypoints'] = coco_keypoints
          ann['num_keypoints'] = num_valid_keypoints
        gt_annotations.append(ann)

  for i, ann in enumerate(gt_annotations):
    ann['id'] = i + 1

  if label_map:
    gt_categories = [{'id': i, 'name': label_map[i]} for i in label_map]
  else:
    category_ids = [gt['category_id'] for gt in gt_annotations]
    gt_categories = [{'id': i} for i in set(category_ids)]

  gt_dataset = {
      'images': gt_images,
      'categories': gt_categories,
      'annotations': copy.deepcopy(gt_annotations),
  }
  return gt_dataset


class COCOGroundtruthGenerator:
  """Generates the ground-truth annotations from a single example."""

  def __init__(self, file_pattern, file_type, num_examples, include_mask,
               regenerate_source_id=False):
    self._file_pattern = file_pattern
    self._num_examples = num_examples
    self._include_mask = include_mask
    self._dataset_fn = dataset_fn.pick_dataset_fn(file_type)
    self._regenerate_source_id = regenerate_source_id

  def _parse_single_example(self, example):
    """Parses a single serialized tf.Example proto.

    Args:
      example: a serialized tf.Example proto string.

    Returns:
      A dictionary of ground-truth with the following fields:
        source_id: a scalar tensor of int64 representing the image source_id.
        height: a scalar tensor of int64 representing the image height.
        width: a scalar tensor of int64 representing the image width.
        boxes: a float tensor of shape [K, 4], representing the ground-truth
          boxes in absolute coordinates with respect to the original image size.
        classes: a int64 tensor of shape [K], representing the class labels of
          each instances.
        is_crowds: a bool tensor of shape [K], indicating whether the instance
          is crowd.
        areas: a float tensor of shape [K], indicating the area of each
          instance.
        masks: a string tensor of shape [K], containing the bytes of the png
          mask of each instance.
    """
    decoder = tf_example_decoder.TfExampleDecoder(
        include_mask=self._include_mask,
        regenerate_source_id=self._regenerate_source_id)
    decoded_tensors = decoder.decode(example)

    image = decoded_tensors['image']
    image_size = tf.shape(image)[0:2]
    boxes = box_ops.denormalize_boxes(
        decoded_tensors['groundtruth_boxes'], image_size)

    source_id = decoded_tensors['source_id']
    if source_id.dtype is tf.string:
      source_id = tf.strings.to_number(source_id, out_type=tf.int64)

    groundtruths = {
        'source_id': source_id,
        'height': decoded_tensors['height'],
        'width': decoded_tensors['width'],
        'num_detections': tf.shape(decoded_tensors['groundtruth_classes'])[0],
        'boxes': boxes,
        'classes': decoded_tensors['groundtruth_classes'],
        'is_crowds': decoded_tensors['groundtruth_is_crowd'],
        'areas': decoded_tensors['groundtruth_area'],
    }
    if self._include_mask:
      groundtruths.update({
          'masks': decoded_tensors['groundtruth_instance_masks_png'],
      })
    return groundtruths

  def _build_pipeline(self):
    """Builds data pipeline to generate ground-truth annotations."""
    dataset = tf.data.Dataset.list_files(self._file_pattern, shuffle=False)
    dataset = dataset.interleave(
        map_func=lambda filename: self._dataset_fn(filename).prefetch(1),
        cycle_length=None,
        num_parallel_calls=tf.data.experimental.AUTOTUNE)

    dataset = dataset.take(self._num_examples)
    dataset = dataset.map(self._parse_single_example,
                          num_parallel_calls=tf.data.experimental.AUTOTUNE)
    dataset = dataset.batch(1, drop_remainder=False)
    dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
    return dataset

  def __call__(self):
    return self._build_pipeline()


def scan_and_generator_annotation_file(file_pattern: str,
                                       file_type: str,
                                       num_samples: int,
                                       include_mask: bool,
                                       annotation_file: str,
                                       regenerate_source_id: bool = False):
  """Scans and generate the COCO-style annotation JSON file given a dataset."""
  groundtruth_generator = COCOGroundtruthGenerator(
      file_pattern, file_type, num_samples, include_mask, regenerate_source_id)
  generate_annotation_file(groundtruth_generator, annotation_file)


def generate_annotation_file(groundtruth_generator,
                             annotation_file):
  """Generates COCO-style annotation JSON file given a ground-truth generator."""
  groundtruths = {}
  logging.info('Loading groundtruth annotations from dataset to memory...')
  for i, groundtruth in enumerate(groundtruth_generator()):
    logging.log_every_n(logging.INFO,
                        'generate_annotation_file: Processing annotation %d',
                        100, i)
    for k, v in six.iteritems(groundtruth):
      if k not in groundtruths:
        groundtruths[k] = [v]
      else:
        groundtruths[k].append(v)
  gt_dataset = convert_groundtruths_to_coco_dataset(groundtruths)

  logging.info('Saving groundtruth annotations to the JSON file...')
  with tf.io.gfile.GFile(annotation_file, 'w') as f:
    f.write(json.dumps(gt_dataset))
  logging.info('Done saving the JSON file...')
